Gokul, J.Nair, M.S.Rajan, J.2026-02-052017Computers and Geosciences, 2017, 109, , pp. 16-24983004https://doi.org/10.1016/j.cageo.2017.07.004https://idr.nitk.ac.in/handle/123456789/25437SAR images are generally corrupted by granular disturbances called speckle, which makes visual analysis and detail extraction a difficult task. Non Local despeckling techniques with probabilistic similarity has been a recent trend in SAR despeckling. To achieve effective speckle suppression without compromising detail preservation, we propose an improvement for the existing Generalized Guided Filter with Bayesian Non-Local Means (GGF-BNLM) method. The proposed method (Guided SAR Image Despeckling with Probabilistic Non Local Weights) replaces parametric constants based on heuristics in GGF-BNLM method with dynamically derived values based on the image statistics for weight computation. Proposed changes make GGF-BNLM method adaptive and as a result, significant improvement is achieved in terms of performance. Experimental analysis on SAR images shows excellent speckle reduction without compromising feature preservation when compared to GGF-BNLM method. Results are also compared with other state-of-the-art and classic SAR depseckling techniques to demonstrate the effectiveness of the proposed method. © 2017 Elsevier LtdHeuristic methodsImage analysisImage denoisingSpeckleSynthetic aperture radarDe-noisingExperimental analysisFeature preservationGuided filteringNon local meansSAR image despecklingSAR imagingSpeckle suppressionRadar imagingBayesian analysisimage analysisprobabilityradar imageryspecklesynthetic aperture radartrend analysis